A Simulation-Optimization Approach for Integrating Physical and Financial Flows in a Supply Chain Under Economic Uncertainty

Abstract

In the last decade, increasing costs and organizational concerns regarding the funding and allocation of financial resources have led to significant attention being given to financial flow and its effects on planning decisions throughout supply chain networks. This study aims to develop a simulation-optimization model to integrate the financial and physical flows in a supply chain planning problem under economic uncertainty. The simulation-optimization model includes a mixed-integer linear programming model and a simulation-based optimization model that are connected through an iterative process. The economic value added (EVA) index is used to measure the financial performance of the supply chain. This study extends the literature on two research domains namely supply chain planning and finance and simulation-optimization modelling for supply chain management. The proposed model applies a scenario approach to cope with economic uncertainty in the supply chain. To demonstrate the efficiency of the proposed model, the performance of the proposed model in solving a test problem from the recent literature is compared with the performance of a conventional simulation-based optimization approach. The results of the study show that the proposed simulation-optimization model outperforms the simulation-based optimization model in all the studied scenarios.

Keywords: System dynamics (SD), Simulation-optimization (S-O), Economic value added (EVA), Asset-liability management, Cash flow management